Learning Reduced Fluid Dynamics
Authors: Zherong Pan, Xifeng Gao, Kui Wu
AAAI 2024 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | Through evaluations on a row of simulation benchmarks, we show that our method reduces the discrepancy by 50-90 percent over conventional reduced models and we outperform PINNs by exactly preserving the time reversibility. |
| Researcher Affiliation | Industry | Lightspeed Studios {zrpan,xifgao,kwwu}@global.tecent.com |
| Pseudocode | No | The paper mentions outlining an algorithm in Appendix 1 but does not present structured pseudocode or an algorithm block in the main text. |
| Open Source Code | No | The paper does not provide any statement or link indicating the public release of source code for the described methodology. |
| Open Datasets | No | The paper uses simulation benchmarks like 'Taylor vortices' and 'smoke plume rise' and states, 'Our training dataset for the POD baseline contains N = 8 trajectories using the full-order dynamics (Equation 1)', but does not provide specific access information (link, DOI, repository, or formal citation with authors/year) for these simulation data. |
| Dataset Splits | No | The paper mentions training and testing data, but does not explicitly describe a validation dataset split or a cross-validation setup. |
| Hardware Specification | Yes | We implement our method using Pytorch with a fluid simulator implemented via native C++ with CPU parallelization, and perform all the computations on an AMD Threadripper 3970X CPU having 32 cores. |
| Software Dependencies | No | The paper states 'We implement our method using Pytorch with a fluid simulator implemented via native C++', but does not provide specific version numbers for Pytorch, C++, or any other software dependencies. |
| Experiment Setup | Yes | We always use a batch size of 1. The performance of our method is summarized in Table 2. We consider two variants of our method: coupled case, where Ckij is treated as a function C(Uk,Ui,Uj) as discussed in Section , and decoupled case, where Ckij is treated as an antisymmetric independent decision variable. ... We experiment with four parameters ϵ = 0.05, 0.01, 0.001, and 0.0001 and the number of bases is p = 8, 11, 16, and 25, correspondingly. ... we set T = 500, δt = 0.01. |